large_metrics {estimators}

Large Sample Metrics

Description

This function performs Monte Carlo simulations to estimate the asymptotic variance - covariance matrix, characterizing the large sample behavior of an estimator. The function evaluates the metrics as a function of a single parameter, keeping the other ones constant. See Details.

Usage

large_metrics(D, prm, est = c("same", "me", "mle"), ...)

Arguments

D

A subclass of Distribution. The distribution family of interest.

prm

A list containing three elements (name, pos, val). See Details.

est

character. The estimator of interest. Can be a vector.

...

extra arguments.

Details

The distribution D is used to specify an initial distribution. The list prm contains details concerning a single parameter that is allowed to change values. The quantity of interest is evaluated as a function of this parameter.

Specifically, prm includes three elements named "name", "pos", and "val". The first two elements determine the exact parameter that changes, while the third one is a numeric vector holding the values it takes. For example, in the case of the Multivariate Gamma distribution, D <- MGamma(shape = c(1, 2), scale = 3) and prm <- list(name = "shape", pos = 2, val = seq(1, 1.5, by = 0.1)) means that the evaluation will be performed for the MGamma distributions with shape parameters ⁠(1, 1)⁠, ⁠(1, 1.1)⁠, ..., ⁠(1, 1.5)⁠ and scale 3. Notice that the initial shape parameter 2 in D is not utilized in the function.

Value

A data.frame with columns "Row", "Col", "Parameter", "Estimator", and "Value".

See Also

small_metrics, plot_small_metrics, plot_large_metrics

Examples

D <- Beta(shape1 = 1, shape2 = 2)

prm <- list(name = "shape1",
            pos = NULL,
            val = seq(0.5, 2, by = 0.5))

x <- large_metrics(D, prm,
                   est = c("mle", "me", "same"))

plot_large_metrics(x)